How to turn a shopfloor agile in its ecosystem as a chamois (mountain goat) in its mountains.
At this end of the year 2025, the European project AI REDGIO 5.0, in which PERNOUD took parts with its experimentation CHAMOis, will end.
AI REDGIO 5.0 is an ambitious EU-funded project, derived from the Made in Europe Partnership, leading the digital transformation of European manufacturing SMEs through Artificial Intelligence at the Edge. Bringing together 43 partners from 18 countries, the project extends the successful legacy of Horizon 2020’s I4MS and AI REDGIO initiatives, which encouraged SMEs to embrace Industry 4.0 technologies.
Among these actors, we find universities, technology providers, technical and competence centers and SMEs with commitment in their transition to industry 4.0 technologies. These SMEs, through experimentation, served as case studies to capitalize on lessons learned and thus help future SMEs to integrate these technologies in a more effective way.
In that ecosystem, the objective of PERNOUD was to set up a decisionmaking tool for the creation and the scheduling of manufacturing sequences. To reach this goal PERNOUD was supported by the competence center Polymeris, its associated DIH (Digital Innovation Hub) Polytronics, the technology provider TXT group and occasionally by Lulea University in Sweden.
After 3 years of diverse and disruptive work, the results for Pernoud are the following:
- First, even if the objective of the project was to integrate 4.0 technologies, we must stay practical and keep in mind what is the industrial objective in order to not use a too advanced technology when a simple one can do it as well. This was the case for us on the scheduling part where the SPEED ORDO solution from IPO35 suits all our needs without real 4.0 technologies.
- Second, manage a project like this opens our mind on what are the benefits and the needs of these new technologies, allowing us to understand how to improve the results of the experiment with regards to the creation of manufacturing sequences but also to imagine new internal applications.
- Third, this project and the ViDi project (EIT Manufacturing) in partnership with Grenoble INP and Groningen University, allows PERNOUD to focus and put in place the necessary training to help their employees in this transition, but also to forecast what will be the future skills to acquire for the industry of tomorrow.
As explained at the beginning of this article, the overall goal of the project is to acquire lessons learned in order to help future SMEs to introduce such technologies. See below some lessons learned from the PERNOUD experimentation:
- Having on site the data/AI expert in order to organize everyday face to face meeting. It is difficult for experts from different fields to get a precise understanding of technical elements from the other field, and face to face meeting make it easier.
- Start simple or small and the extend the scope with success in order to capitalize on the first results and at the end gain lots of time.
- Be practical and take some perspective by restarting from the initial aims and more particularly from the functionality expected. A very complex solution is rarely a good solution.
1. Have a data/AI expert on your team to facilitate daily face-to-face discussions. It can sometimes be difficult to clearly explain a technical element to an expert in another field, and vice versa.
2. Start simply or with a small scope and increase the ambition as the project progresses to capitalize on initial results and ultimately save significant time.
3. Regularly take a step back and revisit the initial objective, particularly the desired functionality, to avoid deploying an overly complex system.
More information : https://www.airedgio5-0.eu/ or https://www.linkedin.com/company/ai-redgio-5-0/posts/?feedView=all
